Thus, the purpose of this work was to find a combination of random noise filters and baseline correction methods that minimized the influence of the background radiation over calibration models for quantifying soil carbon. We compared the performance three filters: Low Pass [1]}, Median [2]}, and
Savitzky-Golay [3]} to remove random noise; and five methods to correct the baseline: Statistics-Sensitive Non-Linear Iterative Peak-Cliping (SNIP) [4]}, [5]}, Top Hat (TH) [6]}, [7]}, Median [7]}, Rolling Balls (RB) [9]}, and 4S Peak Filling (PF) [10]}, which were developed for other photonic techniques such as X ray, Raman, infrared and atomic mass spectroscopy. All combinations of filters and baseline correction methods were optimized with a training soil sample set to get the best correlation with the reference technique. Then all optimized combinations were tested with a separated validation set. After the filtering and the baseline correction, the corrected spectra were used as input variables in the partial least square regression method, and the models built were tested with 10-fold cross-validation. The results were given in the Pearson's correlation coefficient \(\rho \)  and the root mean square error (RMSE) for each combination. The combinations were then ranked according to \(\rho \) .
